Function-guided design of active enzymes

Designing enzymes from functional descriptions remains challenging because catalytic activity is governed by sequence–structure–function relationships. Here we present EnzymeArt, a function-conditioned enzyme-design framework centred on a generative sequence model. EnzymeArt couples function-conditioned sequence generation with structure-guided refinement, annotation checks and substrate-aware computational prioritization to select candidates for synthesis and biochemical testing. Across alcohol dehydrogenase (ADH), malate dehydrogenase (MDH) and triacylglycerol lipase design campaigns, 57 of 60 synthesized designs showed crude-lysate activity above matched background controls. Purified representatives further showed quantitative steady-state catalytic activity. The best designed ADH reached k _cat = 223.7 s ^{− 1} and exceeded a wild-type reference under matched conditions, an MDH reached k _cat = 267.57 s ^{− 1} despite having only 33% sequence identity to its closest BLASTP hit, and a designed lipase hydrolysed both short– and long-chain triglycerides with apparent activity modestly above that of a commercial lipase reference. Together, these results establish a route for converting functional descriptions into experimentally validated enzyme designs with quantitative steady-state kinetic activity.